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Naturally random devices that exploit ambient thermal noise have recentlyattracted attention as hardware primitives for accelerating probabilisticcomputing applications. One such approach is to use a low barrier nanomagnet asthe free layer of a magnetic tunnel junction (MTJ) whose magnetic fluctuationsare converted to resistance fluctuations in the presence of a stable fixedlayer. Here, we propose and theoretically analyze a magnetic tunnel junctionwith no fixed layers but two free layers that are circularly shaped diskmagnets. We use an experimentally benchmarked model that accounts for finitetemperature magnetization dynamics, bias-dependent charge and spin-polarizedcurrents as well as the dipolar coupling between the free layers. We obtainanalytical results for statistical averages of fluctuations that are in goodagreement with the numerical model. We find that the free layers with lowdiameters fluctuate to randomize the resistance of the MTJ in an approximatelybias-independent manner. We show how such MTJs can be used to build a binarystochastic neuron (or a p-bit) in hardware. Unlike earlier stochastic MTJs thatneed to operate at a specific bias point to produce random fluctuations, theproposed design can be random for a wide range of bias values, independent ofspin-transfer-torque pinning. Moreover, in the absence of a carefully optimizedstabled fixed layer, the symmetric double-free layer stack can be manufacturedusing present day Magnetoresistive Random Access Memory (MRAM) technology byminimal changes to the fabrication process. Such devices can be used ashardware accelerators in energy-efficient computing schemes that require alarge throughput of tunably random bits.

Double Free-Layer Magnetic Tunnel Junctions for Probabilistic Bits